Social media threats detectors and Content Analyzers have multiple roles in the platform already described in Section REF .
Given the importance of social media threats, as described in Section , researchers have been studying how to automatically identify them (some examples can be seen in Table REF ).
However, despite the success achieved by these efforts, the robustness of these systems is still limited. For instance, they cannot generalize to new datasets, and resist against attacks (for example, word injection) [1]}, [2]}.
An example of that is the case that occurred in the OffensEval shared task [3]}, where different hate speech classification models were compared in different subtasks.
In that, the best system in Subtask B (i.e. [4]}) ranked the 76th position in Subtask A that is a general and simple case of Subtask B.We highlight that, despite this extreme case, systems tended to maintain a similar performance across the different subtasks. This example stresses how small changes in these tasks may drastically impact system performance informing on the challenge of applying these approaches in the dynamic contexts of social media.
Although identifying threats in social networks is a new field and much work has to be done, it has been receiving much attention. Several shared tasks have been proposed, and each year they become more challenging. Moreover, new evaluation criteria, such as multilingual detection at Task 5 in Semeval 2019 [5]}, different domains at HaSpeeDe in Evalita 2020 [6]}, [7]}, detections at the spam level at Task 5 in Semeval-2021 [8]}, and generalisation to social media platforms other than those used in training at EXIST in IberLEF 2021[9]}, have been included in the datasets.
For example, recent models can generalise the task while maintaining similar results in different platforms and languages under certain conditions [10]}.
